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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/11580

Title: Computational Reconstruction of Cognitive Music Theory
Authors: Tojo, Satoshi
Hirata, Keiji
Hamanaka, Masatoshi
Keywords: Music information processing
Cognitive Thoery of Music
Computational Musicology
Generative Theory of Tonal Music
Issue Date: 2013-01
Publisher: Springer
Magazine name: New Generation Computing
Volume: 31
Number: 2
Start page: 89
End page: 113
DOI: 10.1007/s00354-013-0202-7
Abstract: In order to obtain a computer-tractable model of music, we first discuss what conditions the music theory should satisfy from the various viewpoints of artificial intelligence and/or other computational notions. Then, we look back on the history of cognitive theory of music, i.e., various attempts to represent our mental understandings and to show music structures. Among which, we especially pay attention to the Generative Theory of Tonal Music (GTTM) by Lehrdahl and Jackendoff, as the most promising candidate of cognitive/computational theory of music. We briefly overview the theory as well as its inherent problems, including the ambiguity of its preference rules. By our recent efforts, we have solved this ambiguity problem by assigning parametrized weights, and thus we could implement an automatic tree analyzer. After we introduce the system architecture, we show our application systems.
Rights: This is the author-created version of Springer, Satoshi Tojo, Keiji Hirata, Masatoshi Hamanaka, New Generation Computing, 31(2), 2013, 89-113. The original publication is available at www.springerlink.com, http://dx.doi.org/10.1007/s00354-013-0202-7
URI: http://hdl.handle.net/10119/11580
Material Type: author
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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